Subcortical Brain Volume, Regional Cortical Thickness, and Cortical Surface Area Across Disorders: Findings From the ENIGMA ADHD, ASD, and OCD Working Groups

Boedhoe, Premika S.W.; Rooij, Daan van; Hoogman, Martine; Twisk, Jos W. R.; Schmaal, Lianne; Abe, Yoshinari; Alonso, Pino; Ameis, Stephanie H.; Аникин, А В; Antičević, Alan; Arango, Celso; Arnold, Paul; Asherson, Philip; Assogna, Francesca; Auzias, Guillaume; Banaschewski, Tobias; Баранов, А. А.; Batistuzzo, Marcelo C.; Baumeister, Sarah; Baur‐Streubel, Ramona; Behrmann, Marlene; Bellgrove, Mark A.; Benedetti, Francesco; Beucke, Jan C.; Biederman, Joseph; Bollettini, Irene; Bose, Anushree; Bralten, Janita; Bramati, Ivanei E.; Brandeis, Daniel; Brem, Silvia; Brennan, Brian P.; Busatto, Geraldo F.; Calderoni, Sara; Calvo, Anna; Calvo, Rosa; Castellanos, F. Xavier; Cercignani, Mara; Chaim‐Avancini, Tiffany M.; Chantiluke, Kaylita; Cheng, Yuqi; Cho, Kang Ik K.; Christakou, Anastasia; Coghill, David; Conzelmann, Annette; Cubillo, A.; Dale, Anders M.; Dallaspezia, Sara; Daly, Eileen; Denys, Damiaan; Deruelle, Christine; Martino, Adriana Di; Dinstein, Ilan; Doyle, Alysa E.; Durston, Sarah; Earl, Eric; Ecker, Christine; Ehrlich, Stefan; Ely, Benjamin A.; Epstein, Jeffrey N.; Ethofer, Thomas; Fair, Damien A.; Fallgatter, Andreas J.; Faraone, Stephen V.; Fedor, Jennifer; Feng, Xin; Feusner, Jamie D.; Fitzgerald, Jackie; Fitzgerald, Kate D.; Fouché, Jean-Paul; Freitag, Christine M.; Friðgeirsson, Egill A.; Frodl, Thomas; Gabel, Matt C.; Gallagher, Louise; Gogberashvili, Tinatin Yu.; Gori, Ilaria; Gruner, Patricia; Gürsel, Deniz A.; Haar, Shlomi; Haavik, Jan; Hall, Geoffrey B.; Harrison, Neil A.; Hartman, Catharina A.; Heslenfeld, Dirk J.; Hirano, Yoshiyuki; Hoekstra, Pieter J.; Hoexter, Marcelo Q.; Hohmann, Sarah; Høvik, Marie F.; Hu, Hao; Huyser, Chaim; Jahanshad, Neda; Jalbrzikowski, Maria; James, Anthony; Janssen, Joost; Jaspers‐Fayer, Fern; Jernigan, Terry L.; Kapilushniy, Dmitry; Kardatzki, Bernd; Каркашадзе, Г. А.; Kathmann, Norbert; Kaufmann, Christian; Kelly, Clare; Khadka, Sabin; King, Joseph A.; Koch, Kathrin; Kohls, Gregor; Konrad, Kerstin; Kuno, Masaru; Kuntsi, Jonna; Kvale, Gerd; Kwon, Jun Soo; Lázaro, Luisa; Lera‐Miguel, Sara; Lesch, Klaus‐Peter; Hoekstra, Liesbeth; Liu, Yanni; Löchner, Christine; Louzã, Mário Rodrigues; Luna, Beatríz; Lundervold, Astri J.; Malpas, Charles; Marques, Paulo; Marsh, Rachel; Martínez‐Zalacaín, Ignacio; Mataix‐Cols, David; Mattos, Paulo; McCarthy, Hazel; McGrath, Jane; Mehta, Mitul A.; Menchón, José M.; Mennes, Maarten; Martinho, Mauricio M.; Moreira, Pedro Silva; Morer, Àstrid; Morgado, Pedro; Muratori, Filippo; Murphy, Clodagh; Murphy, Declan; Nakagawa, Akiko; Nakamae, Takashi; Nakao, Tomohiro; Namazova-Baranova, Leyla; Narayanaswamy, Janardhanan C.; Nicolau, Rosa; Nigg, Joel T.; Novotny, Stephanie; Nurmi, Erika L.; Weiß, Eileen Oberwelland; Tuura, Ruth; O’Hearn, Kirsten; O’Neill, Joseph; Oosterlaan, Jaap; Oranje, Bob; Paloyelis, Yannis; Parellada, Mara; Pauli, Paul; Perriello, Chris; Piacentini, John; Piras, Fabrizio; Piras, Federica; Plessen, Kerstin J.; Puig, Olga; Ramos‐Quiroga, Josep Antoni; Reddy, Y.C. Janardhan; Reif, Andreas; Reneman, Liesbeth; Retico, Alessandra; Rosa, Pedro; Rubia, Katya; Rus, Oana Georgiana; Sakai, Yuki; Schrantee, Anouk; Schwarz, Lena A.; Schweren, Lizanne; Seitz, Jochen; Shaw, Philip; Shook, Devon; Silk, Tim; Simpson, Helen Blair; Skokauskas, Norbert; Vila, Juan; Solovieva, Anastasia; Soreni, Noam; Soriano‐Mas, Carles; Spalletta, Gianfranco; Stern, Emily; Stevens, Michael C.; Stewart, S. Evelyn; Sudre, Gustavo; Szeszko, Philip R.; Tamm, Leanne; Taylor, Margot J.; Tolin, David F.; Tosetti, Michela; Tovar‐Moll, Fernanda; Tsuchiyagaito, Aki; Erp, Theo G.M. van; Wingen, Guido A. van; Vance, Alasdair; Venkatasubramanian, Ganesan; Vilarroya, Óscar; Vives‐Gilabert, Yolanda; Polier, Georg G. von; Walitza, Susanne; Wallace, Gregory L.; Wang, Zhen; Wolfers, Thomas; Yoncheva, Yuliya N.; Yun, Je-Yeon; Zanetti, Marcus V.; Zhou, Fengfeng; Ziegler, Georg C.; Zierhut, Kathrin C.; Zwiers, Marcel P.; Thompson, Paul M.; Stein, Dan J.; Buitelaar, Jan K.; Franke, Barbara; Heuvel, Odile A. van den

doi:10.1176/appi.ajp.2020.19030331

Cited by 128 publications

(92 citation statements)

References 42 publications

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“…Converging research evidence indicates that individuals with NDD present with both functional and structural brain abnormalities [65][66][67][68][69][70]. Interestingly, more limited evidence suggests both shared [71] and disorder-specific [72] structural brain alterations that vary over time and differently depending on the specific neurodevelopmental condition [73]. Distinct functional abnormalities have also been described, being more pronounced in ASD compared to other NDDs [74].…”

Section: Discussionmentioning

confidence: 99%

Postural Control in Childhood: Investigating the Neurodevelopmental Gradient Hypothesis

Zoccante

Ciceri

Chamitava

et al. 2021

IJERPH

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Neurodevelopmental disorders (NDDs) have been suggested to lie on a gradient continuum, all resulting from common brain disturbances, but with different degrees of impairment severity. This case-control study aimed to assess postural stability against such hypothesis in 104 children/adolescents aged 5–17, of whom 81 had NDDs and 23 were healthy controls. Compared to healthy controls, Autism Spectrum Disorder (ASD) resulted in the most severely impaired neurodevelopmental condition, followed by Attention Deficit Hyperactive Disorder (ADHD) and Tourette Syndrome (TS). In particular, while ASD children/adolescents performed worse than healthy controls in a number of sensory conditions across all parameters, ADHD children/adolescents performed worse than healthy controls only in the sway area for the most complex sensory conditions, when their vision and somatosensory functions were both compromised, and performance in Tourette Syndrome (TS) was roughly indistinguishable from that of healthy controls. Finally, differences were also observed between clinical groups, with ASD children/adolescents, and to a much lesser extent ADHD children/adolescents, performing worse than TS children/adolescents, especially when sensory systems were not operationally accurate. Evidence from this study indicates that poor postural control may be a useful biomarker for risk assessment during neurodevelopment, in line with predictions from the gradient hypothesis.

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Section: Discussionmentioning

confidence: 99%

Postural Control in Childhood: Investigating the Neurodevelopmental Gradient Hypothesis

Zoccante

Ciceri

Chamitava

et al. 2021

IJERPH

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“…Under the umbrella of the ENIGMA consortium, many independent working groups for specific diseases or phenotypes have been established, including ADHD. By implementing standardized data processing protocols and pipelines, the ENIGMA consortium made it possible to share data across many sites to perform within-disorder and cross-disorder studies (Boedhoe et al, 2020; Paul M. Thompson et al, 2017; P. M. Thompson et al, 2020; P. M. Thompson et al, 2014). The ENIGMA ADHD Working Group has obtained over 4,100 samples of ADHD participants and controls from 37 sites thus far.…”

Section: Discussion and Qualitative Reviewmentioning

confidence: 99%

Machine learning and MRI-based diagnostic models for ADHD: are we there yet?

Zhang-James

Hoogman

Franke

et al. 2020

Preprint

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Machine learning (ML) has been applied to develop magnetic resonance imaging (MRI)-based diagnostic classifiers for attention-deficit/hyperactivity disorder (ADHD). This systematic review examines this literature to clarify its clinical significance and to assess the implications of the various analytic methods applied. We found that, although most of studies reported the classification accuracies, they varied in choice of MRI modalities, ML models, cross-validation and testing methods, and sample sizes. We found that the accuracies of cross-validation methods inflated the performance estimation compared with those of a held-out test, compromising the model generalizability. Test accuracies have increased with publication year but were not associated with training sample sizes. Improved test accuracy over time was likely due to the use of better ML methods along with strategies to deal with data imbalances. Ultimately, large multi-modal imaging datasets, and potentially the combination with other types of data, like cognitive data and/or genetics, will be essential to achieve the goal of developing clinically useful imaging classification tools for ADHD in the future.

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“…Bi-directional causal relationships were also reported between smoking and depression 25 , while depression was found to causally increase alcohol dependence risk without evidence for the reverse 26 . Individuals with psychiatric disorders, including ADHD 27 and depression 28 , are known to have smaller subcortical volumes compared to healthy controls. It may be that causal effects between smoking/alcohol and psychiatric disorders are mediated by subcortical brain volume.…”

Section: Introductionmentioning

confidence: 95%

Investigating the causal nature of the relationship of subcortical brain volume with smoking and alcohol use

Logtenberg¹,

Overbeek²,

Pasman

et al. 2020

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Background: Structural variation in subcortical brain regions has been linked to substance use, including the most prevalent substances nicotine and alcohol. It may be that pre-existing differences in subcortical brain volume affect smoking and alcohol use, but there is also evidence that smoking and alcohol use can lead to structural changes. We assess the causal nature of this complex relationship with bi-directional Mendelian randomization (MR). Methods: MR uses genetic variants predictive of a certain trait (exposure) as instrumental variables to test causal effects on a certain outcome. Due to random assortment at meiosis, genetic variants shouldnt be associated with confounders, allowing less biased causal inference. We employed summary-level data of the largest available genome-wide association studies of subcortical brain region volumes (nucleus accumbens, amygdala, caudate nucleus, hippocampus, pallidum, putamen, and thalamus; n=50,290) and smoking and alcohol use (smoking initiation, n=848,460; cigarettes per day, n=216,590; smoking cessation, n=378,249; alcohol drinks per week, n=630,154; alcohol dependence, n=46,568). The main analysis, inverse-variance weighted regression, was verified by a wide range of sensitivity methods. Results: There was strong evidence that alcohol dependence decreased amygdala and hippocampal volume and that smoking more cigarettes per day decreased hippocampal volume. From subcortical brain volumes to substance use, there was no or weak evidence for causal effects. Conclusions: Our findings suggest that heavy alcohol use and smoking can causally reduce subcortical brain volume. This adds to accumulating evidence that alcohol and smoking affect the brain, and most likely mental health, warranting more recognition in public health efforts.

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Subcortical Brain Volume, Regional Cortical Thickness, and Cortical Surface Area Across Disorders: Findings From the ENIGMA ADHD, ASD, and OCD Working Groups

Cited by 128 publications

References 42 publications

Postural Control in Childhood: Investigating the Neurodevelopmental Gradient Hypothesis

Postural Control in Childhood: Investigating the Neurodevelopmental Gradient Hypothesis

Machine learning and MRI-based diagnostic models for ADHD: are we there yet?

Investigating the causal nature of the relationship of subcortical brain volume with smoking and alcohol use

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